Our lab conducts many studies with different approaches that are jointly geared to understand how intelligent systems encode and retrieve environmental information. At present, we use the human brain as well as artificial neural networks that implement neural-based cognitive operations as our key models of intelligent systems. Our current pet theory is to consider intelligent systems as Bayesian inference machines. In this light, we view the brain as a physical system that dynamically interacts with the environment in which it is embedded, so that it modulates its structure and function in some definable association with environmental variations in information.

Bayesian entropy conversion cycle between brain and environment.

We have three project aims.

1) To develop models of how the brain-environment cycle operates.

2) To obtain empirical experimental proof-of-concept evidence in support of the above theory.

3) To evaluate factors that modulate how a primary neurocognitive mechanism works such as in individual differences in neural processing and cognition.

The bulk of these studies focuses on normative human young and older adult data, with some inclusion of clinical human data where relevant. In addition to deepening our understanding of neural mechanisms, where practical, we also develop applications and interventions that address human neurocognitive issues.